Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning
Abstract Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algori...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-91980-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849767174522011648 |
|---|---|
| author | Kennedy C. Onyelowe Viroon Kamchoom Shadi Hanandeh S. Anandha Kumar Rolando Fabián Zabala Vizuete Rodney Orlando Santillán Murillo Susana Monserrat Zurita Polo Rolando Marcel Torres Castillo Ahmed M. Ebid Paul Awoyera Krishna Prakash Arunachalam |
| author_facet | Kennedy C. Onyelowe Viroon Kamchoom Shadi Hanandeh S. Anandha Kumar Rolando Fabián Zabala Vizuete Rodney Orlando Santillán Murillo Susana Monserrat Zurita Polo Rolando Marcel Torres Castillo Ahmed M. Ebid Paul Awoyera Krishna Prakash Arunachalam |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | Abstract Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R2 of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model’s superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix. |
| format | Article |
| id | doaj-art-e803868c09e846b1bfb8eeae38775f2c |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e803868c09e846b1bfb8eeae38775f2c2025-08-20T03:04:18ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-91980-3Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learningKennedy C. Onyelowe0Viroon Kamchoom1Shadi Hanandeh2S. Anandha Kumar3Rolando Fabián Zabala Vizuete4Rodney Orlando Santillán Murillo5Susana Monserrat Zurita Polo6Rolando Marcel Torres Castillo7Ahmed M. Ebid8Paul Awoyera9Krishna Prakash Arunachalam10Department of Civil Engineering, Michael Okpara University of AgricultureExcellent Center for Green and Sustainable Infrastructure, Department of Civil Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang (KMITL)Department of Civil Engineering, Al-Balqa Applied UniversityDepartment of Civil Engineering, Aditya UniversityFacultad de Recursos Naturales, Escuela Superior Politécnica de Chimborazo (ESPOCH)Facultad de Administracion de Empresas, Escuela Superior Politécnica de Chimborazo (ESPOCH)Facultad de Recursos Naturales, Escuela Superior Politécnica de Chimborazo (ESPOCH)Escuela Superior Politécnica de Chimborazo (ESPOCH), Sede OrellanaDepartment of Civil Engineering, Faculty of Engineering, Future University in EgyptDepartment of Civil Engineering, Prince Mohammad Bin Fahd UniversityDepartamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica MetropolitanaAbstract Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering a potent blend of scientific rigor and computational efficiency. By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. As research continues to refine these models and validate their performance, their adoption promises to revolutionize how concrete materials are engineered, tested, and utilized in construction projects worldwide. In this research work, an extensive literature review, which produced a global representative database for the splitting tensile strength (Fsp) of recycled aggregate concrete, was indulged. The studied concrete components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, and F_type were measured and tabulated. The collected 257 records were partitioned into training set of 200 records (80%) and validation set of 57 records (20%) in line with a more reliable partitioning of database. Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. The results show the Kstar model demonstrates the highest level of performance and reliability among the models, achieving exceptional accuracy with an R2 of 0.96 and Accuracy of 94%. Its RMSE and MAE are both low at 0.15 MPa, indicating minimal deviations between predicted and actual values. Additional metrics such as WI (0.99), NSE (0.96), and KGE (0.96) further confirm the model’s superior efficiency and consistent performance, making it the most dependable tool for practical applications. Also the sensitivity analysis shows that Water content (W) exerts the most significant impact at 40%, demonstrating that the amount of water in the mix is a critical factor for achieving optimal tensile strength. This underscores the need for careful water management to balance workability and strength in sustainable concrete production. Coarse natural aggregate (NCAg) has a substantial impact of 38%, indicating its essential role in maintaining the structural integrity of the concrete mix.https://doi.org/10.1038/s41598-025-91980-3Recycled aggregate concreteSplitting tensile strengthPhysics-informed modelingSustainable constructionConcrete structures |
| spellingShingle | Kennedy C. Onyelowe Viroon Kamchoom Shadi Hanandeh S. Anandha Kumar Rolando Fabián Zabala Vizuete Rodney Orlando Santillán Murillo Susana Monserrat Zurita Polo Rolando Marcel Torres Castillo Ahmed M. Ebid Paul Awoyera Krishna Prakash Arunachalam Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning Scientific Reports Recycled aggregate concrete Splitting tensile strength Physics-informed modeling Sustainable construction Concrete structures |
| title | Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning |
| title_full | Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning |
| title_fullStr | Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning |
| title_full_unstemmed | Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning |
| title_short | Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning |
| title_sort | physics informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning |
| topic | Recycled aggregate concrete Splitting tensile strength Physics-informed modeling Sustainable construction Concrete structures |
| url | https://doi.org/10.1038/s41598-025-91980-3 |
| work_keys_str_mv | AT kennedyconyelowe physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT viroonkamchoom physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT shadihanandeh physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT sanandhakumar physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT rolandofabianzabalavizuete physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT rodneyorlandosantillanmurillo physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT susanamonserratzuritapolo physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT rolandomarceltorrescastillo physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT ahmedmebid physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT paulawoyera physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning AT krishnaprakasharunachalam physicsinformedmodelingofsplittingtensilestrengthofrecycledaggregateconcreteusingadvancedmachinelearning |